Chapter 4-6 PsychStats Pre-Test

1. Using the information from Question 1, what is the appropriate research hypothesis?

μ1 < μ2

μ1 = μ2

M1 > M2

μ1 > μ2

We are predicting that the mean score of population 1 will be greater than population 2. "C" is incorrect, because we use μ to describe the means of the 2 populations. "M" only describes sample means.

Explanation

We are predicting that the mean score of population 1 will be greater than population 2. "C" is incorrect, because we use μ to describe the means of the 2 populations. "M" only describes sample means.

2. In a population, the μ = 5, M = 7, N= 36, and σ = 2.What is μ_m? (WRITE THESE #S DOWN, AS THEY REFER TO THE NEXT QUESTION TOO)

2.5

5

2

5.2

Since μm = μ, μm = 5

Explanation

Since μm = μ, μm = 5

3. A researcher is testing whether students who sleep well the night before an exam have better scores than students who don’t sleep well the night before an exam. Which is “population 1”?

Students who don’t sleep well

Students who do sleep well

Students at Maryville

Students who receive good grades

Our sample population that we are testing is composed of students who do sleep well (pop.1), while we compare them to those who don't sleep well (pop.2)

Explanation

Our sample population that we are testing is composed of students who do sleep well (pop.1), while we compare them to those who don't sleep well (pop.2)

4. Continuing from Question 1, we know μ = 85 and σ = 7. If a student from Population 1 has a score of 95, what can the researcher conclude? (Assume .05 significance level)

Since 1.42 < 1.96, reject the null

Since 1.96 > 1.42, reject the null

Since 1.42 < 1.64, accept the null

Since -1.42 < 1.64, accept the null

Our raw score of 95 falls 1.42 standard deviations above the mean. Since we are doing a one-tailed test, our cutoff point is 1.64 standard deviations above the mean. Since 1.42 is not more extreme than 1.64, we cannot reject the null.

Explanation

Our raw score of 95 falls 1.42 standard deviations above the mean. Since we are doing a one-tailed test, our cutoff point is 1.64 standard deviations above the mean. Since 1.42 is not more extreme than 1.64, we cannot reject the null.

5. Using the information from Question 6, what is σ _m?

.33

1

3.5

1.3

According to the equation σm = σ/√N, 2/√36 = .33

Explanation

According to the equation σm = σ/√N, 2/√36 = .33

6. M= 210, μ= 200, and σ=48. What can we say about the effect size as it compares a sample to the population?

D = -.21; negative effect

D = .21; medium effect

D = .21; small effect

D = -.21; small effect

Following the equation d = (μ1- μ2)/σ, we found (210-200)/48= .21. It is a small effect, because .2=small effect, .5=medium effect, and .8=large effect.

Explanation

Following the equation d = (μ1- μ2)/σ, we found (210-200)/48= .21. It is a small effect, because .2=small effect, .5=medium effect, and .8=large effect.

7. A researcher has made an error in which she has rejected the null hypothesis when she should not have. What type of error is this?

Type III

Type B

Type II

Type I

A Type I error (also called "alpha") occurs when a researcher rejected the null when they shouldn't have. A Type II Error occurs when a researcher does not reject the null when they should have (also called "Beta"). When we reduce the risk of Type I, we increase the risk of Type II (and vice versa).

Explanation

A Type I error (also called "alpha") occurs when a researcher rejected the null when they shouldn't have. A Type II Error occurs when a researcher does not reject the null when they should have (also called "Beta"). When we reduce the risk of Type I, we increase the risk of Type II (and vice versa).

8. In the hypothesis-testing process, we compare the actual sample’s score to:

Null Distribution

Population 1

Comparison Distribution

Kurtotic Distribution

See p.111

Explanation

See p.111

9. Using the information from Question 6, what are the upper and lower confidence limits of a 95% Confidence interval? (Hint: 1.96)

4.35, 5.65

1.08, 8.92

6.35, 7.65

3.08, 10.92

Lower/Upper Limits: M -/+ (1.96)(σm) = 7 -/+ (1.96)(.33) = 6.35, 7.65

Explanation

Lower/Upper Limits: M -/+ (1.96)(σm) = 7 -/+ (1.96)(.33) = 6.35, 7.65

10. Statistical Power is:

The probability that the study will give a significant result if the research hypothesis is false

The probability that the study will give a significant result if the research hypothesis is true

How powerful a study's effect size is

The probability of making a Type I Error

See p. 187

Explanation

See p. 187

11. A researcher believes that by taking vitamins, people will increase the speed at which they read. He tests this hypothesis by timing multiple individuals who have taken vitamins as they read a book. Is this a one-tailed or two-tailed test?

It can be either.

Since he is studying the test by timing people, he is expecting that people who take vitamins will take less time to read than people who don't take vitamins. Therefore, we are actually looking at a one tailed test in the negative direction.

Explanation

Since he is studying the test by timing people, he is expecting that people who take vitamins will take less time to read than people who don't take vitamins. Therefore, we are actually looking at a one tailed test in the negative direction.

12. When the population mean is unknown, the best estimate of the population mean is ______________

The Mean of the Distribution of Means

The Standard Error

The Sample Mean

The Median

The sample mean is the best estimate of the population mean when the population mean is unknown; however, if the population mean IS unknown, we create a Confidence Interval to estimate between what values the true population mean could be.

Explanation

The sample mean is the best estimate of the population mean when the population mean is unknown; however, if the population mean IS unknown, we create a Confidence Interval to estimate between what values the true population mean could be.

Chapter 4-6 Psychstats Pre-test